Welcome! This is the codebase for assignments of our reinforcement learning (RL) course.

Please feel free to open issues if you find anything wrong or confusing in codes or documents in this repository. We will respond to you as soon as possible.

We appreciate you for suggestion and contribution to improve this course!

1. Check the latest release at the time the tutor announced a new assignment is coming.
2. Read the README.md at each assignment directory.
3. Clone or download this repo to get the code at your local computer.
4. Fill the empty functions, slots and cells we left for you. You can search TODO in the files to find them.
5. Follow the instructions in code comments to check if everything work well.
6. Follow the submission instruction in the assignments to submit your work in BruinLearn. (If you are formally enrolled.)

Beautiful code and comment make extra credits. Our aesthetic standard is PEP 8.

You can find local environment setup instruction in assignment 0.

Apart from editing and training locally, you can choose to use Colab. The instruction on how to incorporate Colab into the assignment can be found in our Colab tutorial.

CS260R 2023Fall: Reinforcement Learning. Department of Computer Science at University of California, Los Angeles. Course Instructor: Professor Bolei ZHOU. Assignment author: Zhenghao PENG, Yiran WANG.